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• W2141262421 abstract "Finding cause-effect relationships is the central aim of many studies in the physical, behavioral, social and biological sciences. We consider two well-known mathematical causal models: Structural equation models and causal Bayesian networks. When we hypothesize a causal model, that model often imposes constraints on the statistics of the data collected. These constraints enable us to test or falsify the hypothesized causal model. The goal of our research is to develop efficient and reliable methods to test a causal model or distinguish between causal models using various types of constraints. For linear structural equation models, we investigate the problem of generating a small number of constraints in the form of zero partial correlations, providing an efficient way to test hypothesized models. We study linear structural equation models with correlated errors focusing on the graphical aspects of the models. We provide a set of local Markov properties and prove that they are equivalent to the global Markov property. For causal Bayesian networks, we study equality and inequality constraints imposed on data and investigate a way to use these constraints for model testing and selection. For equality constraints, we formulate an implicitization problem and show how we may reduce the complexity of the problem. We also study the algebraic structure of the equality constraints. For inequality constraints, we present a class of inequality constraints on both nonexperimental and interventional distributions." @default.
• W2141262421 created "2016-06-24" @default.
• W2141262421 creator A5017268916 @default.
• W2141262421 creator A5073863021 @default.
• W2141262421 date "2018-08-10" @default.
• W2141262421 modified "2023-10-16" @default.
• W2141262421 title "Model testing for causal models" @default.
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• W2141262421 doi "https://doi.org/10.31274/etd-180810-1072" @default.
• W2141262421 hasPublicationYear "2018" @default.
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